Texture Classification is the process which used in various pattern recognition applications and classification textures that possess a characteristic appearance. The analysis of texture parameters is a useful way of increasing the information obtainable from images. It is an ongoing field of research, whether these images are medical images or natural. In this work, many classification systems was studied and some variants are proposed and applied to distinguish different types of texture using different discriminating characteristics of textures. These features are derived from the Gray Level Co-occurrence Matrices (GLCM), Run Length Matrices (RLM), Contrast Matrices (CM), and Absolut Gradient Matrices (AGM). Six different sets of features were introduced, among them are: MGLCM, CtdGLCM, MRLM, pCM, pAGM, and CtdAGM; depending on the four traditional methods that was used. The considered methods were applied on two Datasets. The first one consists of 13 classes of textures belong to three sets taken from Salzburg Texture Image Database (i.e., bark, marble and woven fabric), and the Dataset 2 consist of 4 classes of textures taken from skin tissues (normal and abnormal).